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<div class="highlight"><pre><span></span><span class="ch">#!/usr/bin/python</span>
<span class="c1"># The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt</span>
<span class="c1">#</span>
<span class="c1">#   This example shows how to use dlib&#39;s face recognition tool for clustering using chinese_whispers.</span>
<span class="c1">#   This is useful when you have a collection of photographs which you know are linked to</span>
<span class="c1">#   a particular person, but the person may be photographed with multiple other people.</span>
<span class="c1">#   In this example, we assume the largest cluster will contain photos of the common person in the</span>
<span class="c1">#   collection of photographs. Then, we save extracted images of the face in the largest cluster in</span>
<span class="c1">#   a 150x150 px format which is suitable for jittering and loading to perform metric learning (as shown</span>
<span class="c1">#   in the dnn_metric_learning_on_images_ex.cpp example.</span>
<span class="c1">#   https://github.com/davisking/dlib/blob/master/examples/dnn_metric_learning_on_images_ex.cpp</span>
<span class="c1">#</span>
<span class="c1"># COMPILING/INSTALLING THE DLIB PYTHON INTERFACE</span>
<span class="c1">#   You can install dlib using the command:</span>
<span class="c1">#       pip install dlib</span>
<span class="c1">#</span>
<span class="c1">#   Alternatively, if you want to compile dlib yourself then go into the dlib</span>
<span class="c1">#   root folder and run:</span>
<span class="c1">#       python setup.py install</span>
<span class="c1">#</span>
<span class="c1">#   Compiling dlib should work on any operating system so long as you have</span>
<span class="c1">#   CMake installed.  On Ubuntu, this can be done easily by running the</span>
<span class="c1">#   command:</span>
<span class="c1">#       sudo apt-get install cmake</span>
<span class="c1">#</span>
<span class="c1">#   Also note that this example requires Numpy which can be installed</span>
<span class="c1">#   via the command:</span>
<span class="c1">#       pip install numpy</span>

<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">dlib</span>
<span class="kn">import</span> <span class="nn">glob</span>

<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">5</span><span class="p">:</span>
    <span class="k">print</span><span class="p">(</span>
        <span class="s2">&quot;Call this program like this:</span><span class="se">\n</span><span class="s2">&quot;</span>
        <span class="s2">&quot;   ./<a href="face_clustering.py.html">face_clustering.py</a> shape_predictor_5_face_landmarks.dat dlib_face_recognition_resnet_model_v1.dat ../examples/faces output_folder</span><span class="se">\n</span><span class="s2">&quot;</span>
        <span class="s2">&quot;You can download a trained facial shape predictor and recognition model from:</span><span class="se">\n</span><span class="s2">&quot;</span>
        <span class="s2">&quot;    http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2</span><span class="se">\n</span><span class="s2">&quot;</span>
        <span class="s2">&quot;    http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2&quot;</span><span class="p">)</span>
    <span class="nb">exit</span><span class="p">()</span>

<span class="n">predictor_path</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">face_rec_model_path</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="n">faces_folder_path</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="n">output_folder_path</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>

<span class="c1"># Load all the models we need: a detector to find the faces, a shape predictor</span>
<span class="c1"># to find face landmarks so we can precisely localize the face, and finally the</span>
<span class="c1"># face recognition model.</span>
<span class="n">detector</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">get_frontal_face_detector</span><span class="p">()</span>
<span class="n">sp</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">shape_predictor</span><span class="p">(</span><span class="n">predictor_path</span><span class="p">)</span>
<span class="n">facerec</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">face_recognition_model_v1</span><span class="p">(</span><span class="n">face_rec_model_path</span><span class="p">)</span>

<span class="n">descriptors</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">images</span> <span class="o">=</span> <span class="p">[]</span>

<span class="c1"># Now find all the faces and compute 128D face descriptors for each face.</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">faces_folder_path</span><span class="p">,</span> <span class="s2">&quot;*.jpg&quot;</span><span class="p">)):</span>
    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Processing file: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">f</span><span class="p">))</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">load_rgb_image</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>

    <span class="c1"># Ask the detector to find the bounding boxes of each face. The 1 in the</span>
    <span class="c1"># second argument indicates that we should upsample the image 1 time. This</span>
    <span class="c1"># will make everything bigger and allow us to detect more faces.</span>
    <span class="n">dets</span> <span class="o">=</span> <span class="n">detector</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Number of faces detected: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dets</span><span class="p">)))</span>

    <span class="c1"># Now process each face we found.</span>
    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dets</span><span class="p">):</span>
        <span class="c1"># Get the landmarks/parts for the face in box d.</span>
        <span class="n">shape</span> <span class="o">=</span> <span class="n">sp</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>

        <span class="c1"># Compute the 128D vector that describes the face in img identified by</span>
        <span class="c1"># shape.  </span>
        <span class="n">face_descriptor</span> <span class="o">=</span> <span class="n">facerec</span><span class="o">.</span><span class="n">compute_face_descriptor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span>
        <span class="n">descriptors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">face_descriptor</span><span class="p">)</span>
        <span class="n">images</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">img</span><span class="p">,</span> <span class="n">shape</span><span class="p">))</span>

<span class="c1"># Now let&#39;s cluster the faces.  </span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">chinese_whispers_clustering</span><span class="p">(</span><span class="n">descriptors</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="n">num_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">labels</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Number of clusters: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_classes</span><span class="p">))</span>

<span class="c1"># Find biggest class</span>
<span class="n">biggest_class</span> <span class="o">=</span> <span class="bp">None</span>
<span class="n">biggest_class_length</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">):</span>
    <span class="n">class_length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">([</span><span class="n">label</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">labels</span> <span class="k">if</span> <span class="n">label</span> <span class="o">==</span> <span class="n">i</span><span class="p">])</span>
    <span class="k">if</span> <span class="n">class_length</span> <span class="o">&gt;</span> <span class="n">biggest_class_length</span><span class="p">:</span>
        <span class="n">biggest_class_length</span> <span class="o">=</span> <span class="n">class_length</span>
        <span class="n">biggest_class</span> <span class="o">=</span> <span class="n">i</span>

<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Biggest cluster id number: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">biggest_class</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Number of faces in biggest cluster: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">biggest_class_length</span><span class="p">))</span>

<span class="c1"># Find the indices for the biggest class</span>
<span class="n">indices</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
    <span class="k">if</span> <span class="n">label</span> <span class="o">==</span> <span class="n">biggest_class</span><span class="p">:</span>
        <span class="n">indices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Indices of images in the biggest cluster: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">indices</span><span class="p">)))</span>

<span class="c1"># Ensure output directory exists</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">output_folder_path</span><span class="p">):</span>
    <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_folder_path</span><span class="p">)</span>

<span class="c1"># Save the extracted faces</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Saving faces in largest cluster to output folder...&quot;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">indices</span><span class="p">):</span>
    <span class="n">img</span><span class="p">,</span> <span class="n">shape</span> <span class="o">=</span> <span class="n">images</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
    <span class="n">file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_folder_path</span><span class="p">,</span> <span class="s2">&quot;face_&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
    <span class="c1"># The size and padding arguments are optional with default size=150x150 and padding=0.25</span>
    <span class="n">dlib</span><span class="o">.</span><span class="n">save_face_chip</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">file_path</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
    
    
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